As If Biomarker Discovery Isn't Hard Enough: The Consequences of Poorly Characterized Reagents
Open accessAbstract
The advent of high-throughput “-omic” technologies over the past 2 decades has driven a vast expansion in the search for clinical biomarkers, as manifested by the plethora of publications on biomarker discovery (over 8600) listed on PubMed since 2000. Unfortunately, the same time period has seen a relative dearth of clinically validated biomarkers that have received Food and Drug Administration clearance; only 10 new cancer biomarkers have been cleared by the Food and Drug Administration in the same time period (1). Although it is technically fairly straightforward to identify differentially expressed genes and gene products that are statistically associated with various clinical conditions, it is much more difficult to convert statistical associations into clinically useful biomarkers that meet the required performance characteristics of specificity, sensitivity, and reliability that are required for most clinical applications. This problem is particularly acute in the case of biomarkers for early detection of cancer, given the relatively low prevalence of many cancers and the substantial consequences of both false-negative and false-positive results. Early detection of ovarian cancer is a particularly illustrative example. The discrepancy in clinical outcome between stage I/II ovarian cancer and stage III/IV is compelling, with >85% survival for stage I ovarian cancer, but <20% survival at 5 years for stage III/IV. However, almost 85% of all ovarian cancer patients are diagnosed at stage III/IV (2, 3). With the prevalence of ovarian cancer being only about 1 in 10 000, biomarkers for its early detection must demonstrate specificity >99% at sensitivities >80% (4).
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Cites
Phases of Biomarker Development for Early Detection of Cancer
Pepe, M. S., Etzioni, R., Feng, Z., Potter, J. D., Thompson, M. L., Thornquist, M., Winget, M., & Yasui, Y. (2001) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/93.14.1054 · Cited by 1,686 · see citing works
Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges
Füzéry, A. K., Levin, J. D., Chan, M. M., & Chan, D. W. (2013) · Clinical Proteomics · doi:10.1186/1559-0275-10-13 · Cited by 428 · see citing works
Shi, T., Fillmore, T., Sun, X., Zhao, R., Schepmoes, A., Hossain, M., Xie, F., Wu, S., Kim, J., Jones, N., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1204366109 · Cited by 220 · see citing works
Self-citationZhu, C. S., Pinsky, P. F., Cramer, D. W., Ransohoff, D. F., Hartge, P. A., Pfeiffer, R. M., Urban, N. D., Mor, G., Bast, R. C., Moore, L. E., et al. (2011) · Cancer Prevention Research · doi:10.1158/1940-6207.capr-10-0193 · Cited by 188 · see citing works
Diamandis, E. P. (2012) · BMC Medicine · doi:10.1186/1741-7015-10-87 · Cited by 186 · see citing works
Kuhn, E. W., Whiteaker, J. R., Mani, D. R., Jackson, A., Zhao, L., Pope, M. E., SMITH, D. S. H., Rivera, K. D., Anderson, N. L., Skates, S. J., et al. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.013854 · Cited by 183 · see citing works
Ovarian cancer: risk factors, treatment and management
Rooth, C. (2013) · British Journal of Nursing · doi:10.12968/bjon.2013.22.sup17.s23 · Cited by 149 · see citing works
Ovarian cancer: the duplicity of CA125 measurement
Karam, A., & Karlan, B. Y. (2010) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2010.44 · Cited by 95 · see citing works
Skates, S. J. (2012) · International Journal of Gynecological Cancer · doi:10.1097/igc.0b013e318256488a · Cited by 80 · see citing works
Feng, Z., Kagan, J., PEPE, M. S., Thornquist, M. D., Rinaudo, J. A., Dahlgren, J., Krueger, K. E., Zheng, Y., Patriotis, C., Huang, Y., et al. (2012) · Clinical Chemistry · doi:10.1373/clinchem.2012.185140 · Cited by 55 · see citing works
Prassas, I., Brinc, D., Farkona, S., Leung, F., Dimitromanolakis, A., Chrystoja, C. C., Brand, R., Kulasingam, V., Blasutig, I. M., & Diamandis, E. P. (2014) · Clinical Chemistry · doi:10.1373/clinchem.2013.215236 · Cited by 52 · see citing works
Bioinformatic identification of proteins with tissue-specific expression for biomarker discovery
Prassas, I., Chrystoja, C. C., Makawita, S., & Diamandis, E. P. (2012) · BMC Medicine · doi:10.1186/1741-7015-10-39 · Cited by 52 · see citing works
Why have protein biomarkers not reached the clinic?
Hanash, S. (2011) · Genome Medicine · doi:10.1186/gm282 · Cited by 40 · see citing works
Statement of Concern about a Commercial Assay Used to Measure Soluble Hemojuvelin in Humans
Gutiérrez, O. M., Sun, C. C., Chen, W., Babitt, J. L., & Lin, H. Y.-F. (2012) · American Journal of Nephrology · doi:10.1159/000342519 · Cited by 18 · see citing works
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Advances in targeted proteomics and applications to biomedical research
Shi, T., Song, E., Nie, S., Rodland, K., Liu, T., Qian, W., & Smith, R. D. (2016) · PROTEOMICS · doi:10.1002/pmic.201500449 · Cited by 259 · see citing works
Self-citationAltered β-Cell Prohormone Processing and Secretion in Type 1 Diabetes
Calvo, T. R., Chen, Y., Verchere, C. B., Haataja, L., Arvan, P. R., Leete, P., Richardson, S. J., Morgan, N. G., Qian, W., Pugliese, A., et al. (2021) · Diabetes · doi:10.2337/dbi20-0034 · Cited by 51 · see citing works
Application of capillary electrophoresis for the early diagnosis of cancer
Yang, Z., & Sweedler, J. V. (2014) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-014-7722-y · Cited by 35 · see citing works
Misspellings or “miscellings”—Non‐verifiable and unknown cell lines in cancer research publications
Oste, D. J., Pathmendra, P., Richardson, R. A. K., Johnson, G., Ao, Y., Arya, M. D., Enochs, N. R., Hussein, M., Kang, J., Lee, A., et al. (2024) · International Journal of Cancer · doi:10.1002/ijc.34995 · Cited by 18 · see citing works
Selby, P. J., Banks, R. E., Gregory, W. M., Hewison, J., Rosenberg, W. M., Altman, D. G., Deeks, J. J., McCabe, C. J., Parkes, J., Sturgeon, C. M., et al. (2018) · Programme Grants for Applied Research · doi:10.3310/pgfar06030 · Cited by 12 · see citing works
Garrigan, C., Han, J., Tolomeo, P., Johnson, K. J., Master, S. R., Lautenbach, E., & Nachamkin, I. (2016) · PubMed Central · Cited by 2 · see citing works
- Authors
- Rodland, K. D.
- Affiliations
- Pacific Northwest National Laboratory (US)
- Corresponding author
- Rodland, Karin D
- ORCID
- Rodland, Karin D
- Venue
- Clinical Chemistry
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-02-01
- Volume
- 60
- Issue
- 2
- Pages
- 290-291
- ISSN
- 0009-9147 (ISSN-L)
- 1530-8561
- PMC
- PMC4104817
- OpenAlex
- W2134657394
- Cited by
- 6
- References
- 14
- Citations by year
- 2024: 1 · 2021: 1 · 2018: 1 · 2016: 2 · 2014: 1
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Clinical Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (U24CA160019)
- Topics
- Health Sciences › Medicine › Physiology › Clinical Laboratory Practices and Quality Control
- Health Sciences › Medicine › Reproductive Medicine › Ovarian cancer diagnosis and treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Keywords
- biomarker discovery, early cancer detection, ovarian cancer, false positives, false-negative results, high-throughput omics
- MeSH terms
- Humans, Neoplasm Staging, Neoplasms, Reagent Kits, Diagnostic, Sensitivity and Specificity, Biomarkers, Early Diagnosis, High-Throughput Screening Assays
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